We study the question of testing structured properties (classes) of discrete distributions.
Specifically, given sample access to an arbitrary distribution D over n and a property PP, the …

We present a new methodology for proving distribution testing lower bounds, establishing a
connection between distribution testing and the simultaneous message passing (SMP) …

We study distribution testing in the standard access model and the conditional access model
when the memory available to the testing algorithm is bounded. In both scenarios, the …

We show how any PAC learning algorithm that works under the uniform distribution can be
transformed, in a blackbox fashion, into one that works under an arbitrary and unknown …

The framework of distribution testing is currently ubiquitous in the field of property testing. In
this model, the input is a probability distribution accessible via independently drawn …

Distribution testing deals with what information can be deduced about an unknown
distribution over $\{1,\ldots, n\} $, where the algorithm is only allowed to obtain a relatively …

We investigate tolerant testing and distance estimation problems in distribution testing when
the amount of memory is limited. In particular, our aim is to provide a good estimate of the …

The framework of distribution testing is currently ubiquitous in the field of property testing. In
this model, the input is a probability distribution accessible via independently drawn …